Efficient Learning Method to Connect Observables
Abstract
Constructing fast and accurate surrogate models is a key ingredient for making robust predictions in many topics. We introduce a new model, the multiparameter eigenvalue problem (MEP) emulator. The new method connects emulators and can make predictions directly from observables to observables. We show that the MEP emulator can be trained with data from eigenvector continuation and parametric matrix model emulators. A simple simulation on a one-dimensional lattice confirms the performance of the MEP emulator. Using ^{28}O as an example, we also demonstrate that the predictive probability distribution of the target observables can be straightforwardly obtained through the new emulator.
Fields of Study & Subjects
Educational Technology and Assessment
Intelligent Tutoring Systems and Adaptive Learning
Publication details
- Journal / source
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Volume
- 136
- Issue
- 20
- Pages
- 202502–202502
- Publication date
- 21 April 2026
- Language
- EN